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An Adaptive Object Tracking Using Kalman Filter and Probability Product Kernel

Authors:

Hamd Ait Abdelali,

Fedwa Essannouni,

Leila Essannouni,

Driss AboutajdineAuthors Info & Claims

Modelling and Simulation in Engineering, Volume 2016

Page 4

https://doi.org/10.1155/2016/2592368

Published: 01 March 2016 Publication History

Abstract

We present a new method for object tracking; we use an efficient local search scheme based on the Kalman filter and the probability product kernel KFPPK to find the image region with a histogram most similar to the histogram of the tracked target. Experimental results verify the effectiveness of this proposed system.

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Cited By

Jo KIm JKim DGrygiel KCerny TKim SWang W(2017)Comparison of single and deep long short-term memory for single object trackingProceedings of the International Conference on Research in Adaptive and Convergent Systems10.1145/3129676.3129681(85-88)Online publication date: 20-Sep-2017
https://dl.acm.org/doi/10.1145/3129676.3129681
Firouznia MFaez KAmindavar HAlikhani Koupaei JPantano PBilotta E(2017)Multi-step prediction method for robust object trackingDigital Signal Processing10.1016/j.dsp.2017.07.02470:C(94-104)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.dsp.2017.07.024
Hassan AAffandi A(2016)On Modelling and Comparative Study of LMS and RLS Algorithms for Synthesis of MSAModelling and Simulation in Engineering10.1155/2016/97424832016(6)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1155/2016/9742483

Index Terms

An Adaptive Object Tracking Using Kalman Filter and Probability Product Kernel
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
        Tracking
      2. Computer vision tasks
        Scene understanding

Index terms have been assigned to the content through auto-classification.

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cover image Modelling and Simulation in Engineering

Modelling and Simulation in Engineering Volume 2016, Issue

March 2016

63 pages

ISSN:1687-5591

EISSN:1687-5605

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Copyright © Copyright © 2016 Hamd Ait Abdelali et al.

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Hindawi Limited

London, United Kingdom

Publication History

Published: 01 March 2016

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Cited By

Jo KIm JKim DGrygiel KCerny TKim SWang W(2017)Comparison of single and deep long short-term memory for single object trackingProceedings of the International Conference on Research in Adaptive and Convergent Systems10.1145/3129676.3129681(85-88)Online publication date: 20-Sep-2017
https://dl.acm.org/doi/10.1145/3129676.3129681
Firouznia MFaez KAmindavar HAlikhani Koupaei JPantano PBilotta E(2017)Multi-step prediction method for robust object trackingDigital Signal Processing10.1016/j.dsp.2017.07.02470:C(94-104)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.dsp.2017.07.024
Hassan AAffandi A(2016)On Modelling and Comparative Study of LMS and RLS Algorithms for Synthesis of MSAModelling and Simulation in Engineering10.1155/2016/97424832016(6)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1155/2016/9742483

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